Genetic Programming for Generative Learning and Recognition of Hand-Drawn Shapes

@InCollection{Jaskowski:2009:EIASP,
author = "Wojciech Jaskowski and Krzysztof Krawiec and
Bartosz Wieloch",
title = "Genetic Programming for Generative Learning and
Recognition of Hand-Drawn Shapes",
booktitle = "Evolutionary Image Analysis and Signal Processing",
publisher = "Springer",
year = "2009",
editor = "Stefano Cagnoni",
volume = "213",
series = "Studies in Computational Intelligence",
pages = "73--90",
address = "Berlin / Heidelberg",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-01635-6",
ISSN = "1860-949X",
DOI = "doi:10.1007/978-3-642-01636-3_5",
abstract = "We propose a novel method of evolutionary visual
learning that uses a generative approach to assess the
learner's ability to recognise image contents. Each
learner, implemented as a genetic programming (GP)
individual, processes visual primitives that represent
local salient features derived from the input image.
The learner analyses the visual primitives, which
involves mostly their grouping and selection,
eventually producing a hierarchy of visual primitives
build upon the input image. Based on that it provides
partial reproduction of the shapes of the analysed
objects and is evaluated according to the quality of
that reproduction.We present the method in detail and
verify it experimentally on the real-world task of
recognition of hand-drawn shapes. In particular, we
show how GP individuals trained on examples from
different decision classes can be combined to build a
complete multiclass recognition system. We compare such
recognition systems to reference methods, showing that
our generative learning approach provides similar
results. This chapter also contains detailed analysis
of processing carried out by an exemplary individual.",
notes = "Institute of Computing Science, Poznan University of
Technology,Poland EvoISAP, EvoNET, EvoStar",
}